A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India)

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

A synergistic approach towards understanding flood risks over coastal multi-hazard environments: Appraisal of bivariate flood risk mapping through flood hazard, and socio-economic-cum-physical vulnerability dimensions

The dynamics of flood risk over Coastal Multi-hazard Catchments (CMC) exhibit bizarre characteristics. In these regions, flood hazards are governed by a complex interaction of multiple flood-inducing sources; varying in magnitudes, origin, and direction of propagation. Our conventional understanding of vulnerability may be obscure within these catchments. This can be attributable to the heterogeneous nature of various physical and socio-economic entities. The study proposes a comprehensive framework to quantify bivariate flood risks over a severely flood-prone region in India. The study considers flood hazards, along with vulnerabilities transpiring from (a) physical, (b) socio-economic, and (c) composite (combination of both) groups of indicators. To overcome data scarcity prevalent in CMCs, CHIRPS v2.0, a high-resolution Satellite Precipitation Product, along with other ancillary datasets, are forced to 1D2D coupled MIKE+ hydrodynamic model to simulate flood hazards. A set of 24 indicators are considered within the Shannon Entropy-cum-TOPSIS framework to derive three types of vulnerability. The marginal and compound contributions of hazard and each vulnerability type are represented through a novel concept of bivariate flood risk classifier at the village scale. We notice high and very-high flood hazards over the coastline and floodplains. An equitable influence of socio-economic vulnerability and hazards is reflected, as they cover 41 % of villages together under varied degrees of flood risks. The impacts of hazards are underscored in the presence of physical vulnerability, as the latter contributes to risks in about 72 % of villages. Composite vulnerability prevails its impact over 53 % of villages, dominating its influence on flood risks over hazards. The study delivers vital information to the global flood management community on the prudent selection of indicators, as their influence is markedly noticed on the overall flood risks. The diversified characteristics of flood risk inspire a rationalized implementation of structural and non-structural options in resource-constrained conditions.

Are open-source hydrodynamic models efficient in quantifying flood risks over mountainous terrains? An exhaustive analysis over the Hindu-Kush-Himalayan region

The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly.

Multiparameter flood hazard, socioeconomic vulnerability and flood risk assessment for densely populated coastal city

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies.

Can atmospheric reanalysis datasets reproduce flood inundation at regional scales? A systematic analysis with ERA5 over Mahanadi River Basin, India

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95th and 99th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience.

Framework for global sensitivity analysis in a complex 1D-2D coupled hydrodynamic model: Highlighting its importance on flood management over large data-scarce regions

Sensitivity analysis determines how perturbation or variation in the values of an independent variable affects a particular dependent variable. The present study attempts to comprehend the sensitivity of the static input parameters on the accuracy of the outputs in a hydrodynamic flood model, which subsequently improves the model accuracy. Hydrodynamic flood modeling is computationally strenuous and data-intensive. Moreover, the accuracy of the flood model outputs is extremely sensitive to the quality of hydrologic and hydraulic inputs, along with a set of static parameters that are traditionally assumed and primarily used for calibration. Therefore, we focus on developing a framework for global sensitivity analysis (GSA) of static input parameters in a 1D-2D coupled hydrodynamic flood modeling system. A set of numerical experiments is conducted by perturbing various combinations of input parameters from their standard (or observed) values to generate flow hydrographs. Nonparametric probability density functions (PDFs) of the river discharge at different locations are compared to calculate the Kullback-Leibler (KL) entropy or KL-divergence, which is used to quantify the sensitivity of the input parameters. We demonstrated the proposed framework on a highly flood-prone rural catchment of the Shilabati River in West Bengal, India, and infer that the sensitivity of the static input parameters is highly dynamic, and their importance varies spatially from the upstream to the downstream of the river. However, Manning's n values of the channel and the banks are significantly sensitive irrespective of the location in the river reach. We suggest that any flood modeling exercise should accompany a GSA, which sets a guideline for the modelers to prioritize the set of sensitive static input parameters during data monitoring, collection, and retrieval. This study is the first attempt at a GSA in a 1D-2D coupled hydrodynamic flood modeling system, whose importance cannot be over-emphasized in any flood modeling platform. The proposed novel framework is generic and can be implemented prior to flood risk analyses for any floodplain management exercise. All free and commercially-available flood models can incorporate the proposed framework for a GSA as an extension toolbox.

Assessing and optimizing the hydrological performance of Grey-Green infrastructure systems in response to climate change and non-stationary time series

Climate change has led to the increased intensity and frequency of extreme meteorological events, threatening the drainage capacity in urban catchments and densely built-up cities. To alleviate urban flooding disasters, strategies coupled with green and grey infrastructure have been proposed to support urban stormwater management. However, most strategies rely largely on diachronic rainfall data and ignore long-term climate change impacts. This study described a novel framework to assess and to identify the optimal solution in response to uncertainties following climate change. The assessment framework consists of three components: (1) assess and process climate data to generate long-term time series of meteorological parameters under different climate conditions; (2) optimise the design of Grey-Green infrastructure systems to establish the optimal design solutions; and (3) perform a multi-criteria assessment of economic and hydrological performance to support decision-making. A case study in Guangzhou, China was carried out to demonstrate the usability and application processes of the framework. The results of the case study illustrated that the optimised Grey-Green infrastructure could save life cycle costs and reduce total outflow (56-66%), peak flow (22-85%), and TSS (more than 60%) compared to the fully centralised grey infrastructure system, indicating its high superior in economic competitiveness and hydrological performance under climate uncertainties. In terms of spatial configuration, the contribution of green infrastructure appeared not as critical as the adoption of decentralisation of the drainage networks. Furthermore, under extreme drought scenarios, the decentralised infrastructure system exhibited an exceptionally high degree of removal performance for non-point source pollutants.

Indicator-based approach for fluvial flood risk assessment at municipal level in Slovakia

The article focuses on the mapping and assessment of fluvial flood risk at municipal level of Slovakia. The fluvial floods risk index (FFRI), composed of a hazard component and a vulnerability component, was computed for 2927 municipalities using spatial multicriteria analysis and geographic information systems (GIS). The fluvial flood hazard index (FFHI) was computed based on eight physical-geographical indicators and land cover representing the riverine flood potential and also the frequency of flood events in individual municipalities. The fluvial flood vulnerability index (FFVI) was calculated using seven indicators representing the economic and social vulnerability of municipalities. All of the indicators were normalized and weighted using the rank sum method. By aggregating the weighted indicators, we obtained the FFHI and FFVI in each municipality. The final FFRI is a result of a synthesis of the FFHI and FFVI. The results of this study can be used mainly in the framework of flood risk management at national spatial scale, but also for local governments and periodic update of the Preliminary Flood Risk Assessment document, which is carried out at the national level under the EU Floods Directive.

A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen's kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district's relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

A Comprehensive Approach for Floodplain Mapping through Identification of Hazard Using Publicly Available Data Sets over Canada

Quantifying flood inundation and hazards over large regions is paramount for gaining critical information on flood risk over the vulnerable population and environment. Readily available global data and enhancement in computational simulations have made it easier to simulate flooding at a large scale. This study explores the usability of publicly available datasets in flood inundation and hazard mapping, and ensures the flood-related information reaches the end-users efficiently. Runoff from the North American Regional Reanalysis and other relevant inputs are fed to the CaMa-Flood model to generate flooding patterns for 1 in 100 and 1 in 200-year return period events over Canada. The simulated floodplain maps are overlaid on the property footprints of 34 cities (falling within the top 100 populated cities of Canada) to determine the degree of exposure during 1991, 2001 and 2011. Lastly, Flood Map Viewer—a web-based public tool, is developed to disseminate extensive flood-related information. The development of the tool is motivated by the commitment of the Canadian government to contribute $63 M over the next three years for the development of flood maps, especially in high-flood risk areas. The results from the study indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-year event, which is roughly 4 percent more than observed during the 100-year event. We notice an increase in the properties exposed to flooding during the last three decades, with a signature rise in Toronto, Montreal and Edmonton. The flood-related information derived from the study can be used along with vulnerability and exposure components to quantify flood risk. This will help develop appropriate pathways for resilience building for long-term sustainable benefits.

Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management

The present study intends to improve the robustness of a flood susceptibility (FS) model with a small number of parameters in data-scarce areas, such as northwest Bangladesh, by employing machine learning-based sensitivity analysis and an analytical hierarchy process (AHP). In this study, the nine most relevant flood elements (such as distance from the river, rainfall, and drainage density) were chosen as flood conditioning variables for modeling. The FS model was produced using AHP technique. We used an empirical and binormal receiver operating characteristic (ROC) curves for validating the models. We performed Sensitivity analyses using a random forest (RF)-based mean Gini decline (MGD), mean decrease accuracy (MDA), and information gain ratio to find out the sensitive flood conditioning variables. After performing sensitivity analysis, the least sensitivity variables were eliminated. We re-ran the model with the rest of the parameters to enhance the model’s performance. Based on previous studies and the AHP weighting approach, the general soil type, rainfall, distance from river/canal (Dr), and land use/land cover (LULC) had higher factor weights of 0.22, 0.21, 0.19, and 0.15, respectively. The FS model without sensitivity and with sensitivity performed well in the present study. According to the RF-based sensitivity and information gain ratio, the most sensitive factors were rainfall, soil type, slope, and elevation, while curvature and drainage density were less sensitive parameters, which were excluded in re-running the FS model with just vital parameters. Using empirical and binormal ROC curves, the new FS model yields higher AUCs of 0.835 and 0.822, respectively. It is discovered that the predicted model’s robustness may be maintained or increased by removing less relevant factors. This study will aid decision-makers in developing flood management plans for the examined region.

Drought vulnerability and risk assessment in India: Sensitivity analysis and comparison of aggregation techniques

Long term drought management requires proper assessment and characterization of drought hazard, vulnerability and risk. This is particularly important for an agriculture-dependent, highly-populated, developing country such as India. However, the regulation of drought vulnerability and drought risk assessment in the country is mostly region-specific and ad-hoc, considering only a limited number of vulnerability indicators. In this study, a comprehensive, fine-resolution, country-wide drought risk assessment is carried out considering drought hazard in a multivariate framework, and using reliable drought vulnerability indicators that account for exposure, sensitivity and adaptive capacity. Further, multiple aggregation techniques including subjective, objective and comprehensive methods are employed for vulnerability assessment, and their performance assessed and compared. The Analytic Hierarchy Process (AHP)+Entropy and TOPSIS methods, which are comprehensive aggregation techniques are found to be better performing, TOPSIS being the most robust method. A bivariate choropleth map based on the TOPSIS-derived drought vulnerability shows regions of Punjab, Haryana, Uttar Pradesh and Tamil Nadu subjected to drought hazard-driven risk, while risk in other regions such as Rajasthan, parts of Central India, Orissa and parts of Maharashtra are driven more by drought vulnerability. Parts of Western Rajasthan, Vidharbha, North-East India, Chattisgarh, Tamil Nadu and Karnataka are under severe drought risk resulting from an interplay of hazard and vulnerability. Irrigation index, water body fraction, and groundwater availability are found to be the most significant indicators for assessing drought vulnerability in India. The above findings can aid decision makers and government bodies to plan region-specific line of action for building drought resilience.

Changes in floodplain regimes over Canada due to climate change impacts: Observations from CMIP6 models

With the recent Coupled Model Intercomparison Project Phase 6 (CMIP6), water experts and flood modellers are curious to explore the efficacy of the new and upgraded climate models in representing flood inundation dynamics and how they will be impacted in the future by climate change. In this study, for the first time, we consider the latest group of General Circulation Models (GCMs) from CMIP6 to examine the probable changes in floodplain regimes over Canada. A set of 17 GCMs from Shared Socioeconomic Pathways (SSPs) 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1980 to 2019), near-future (2021 to 2060), and far-future (2061 to 2100) time-periods are selected. A comprehensive framework consisting of hydrodynamic flood modelling, and statistical experiments are put forward to derive high-resolution Canada-wide floodplain maps for 100 and 200-yr return periods. The changes in floodplain regimes for the future periods are analyzed over drainage basin scale in terms of (i) changes in flood inundation extents, (ii) changes in flood hazards (high and very-high classes), and (iii) changes in flood frequency. Our results show a significant rise (>30%) in flood inundation extents in the future periods; particularly intense over western and eastern regions. The flood hazards are expected to cover ~16% more geographical area of Canada. We also find that large areas in northern and western Canada and a few spots in the eastern parts of Canada will be getting flooded more frequently compared to the historical period. The observations derived from this study are vital for enhancing flood preparedness, optimal land-use planning, and refurbishing both structural and non-structural flood control options for improved resilience. The study instills new knowledge on revamping the existing flood management approaches and adaptation strategies for future protection.

Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models

Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.

WebFRIS: An efficient web-based decision support tool to disseminate end-to-end risk information for flood management

The present study describes the development of a web-based flood risk information system 'WebFRIS' for Jagatsinghpur district, a severely flood-prone region in Eastern India. The WebFRIS is designed by using various readily available open-source web tools and packages such as Google Map, PHP, MySQL, and JSON. Special emphasis is directed towards designing the layout and architecture, to be easily accessible by any end-user irrespective of any technical know-how. The WebFRIS illustrates spatial maps of flood hazard, socio-economic vulnerability, and flood risk at the village level for two-time scenarios. While analyzing a set of graphical statistics depicting the changes in flood risk components, a significant increase in high and very-high categories of both flood hazard (~140%) and socio-economically vulnerable villages (~68%) is noticed during Scenario-I. The number of villages facing compound risk (contributed equally by flood hazard and socio-economic vulnerability) nearly doubled in Scenario-I. A spatial analysis of diametric changes in flood risk shows that a large proportion of villages in Balikuda, Ersama, and Tirtol tehsils have undergone radical changes. Following these observations, a set of possible engineering, social, and policy measures are proposed, whose implementation in the near future is expected to reinforce flood management in the study area. The WebFRIS architecture is flexible, easy-to-use; it is expected to provide crucial lessons to the local bodies, town-planners, water professionals, flood experts, and also the citizens, a precious knowledge on flood risk management. The WebFRIS may be considered as a precious cartographic product for environmental management. The proposed web platform is generic, as it can be applied to study other inter-related systems such as environmental protection, land-use planning, coastal habitat restoration, and community resilience building.

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.

Level-set methods applied to the kinematic wave equation governing surface water flows

Critical issues arising from the governing nonlinear equations in surface water hydrodynamic include discontinuities in water surface levels, blow-up of water surface gradient, and treatment of dry beds or zero water depths, involving mathematical problems related to functional regularities of unknown variables such as the water depth. The level-set method is a powerful approach to relax requirements for functional regularities of unknowns in nonlinear partial differential equations of first order. In this study, the level-set method is applied to the one-dimensional kinematic wave equation, resulting in a linear level-set equation of the first order in a two-dimensional space to tackle dry beds. The zeros of the level-set function represent the water depths. Hypothesizing that the level-set function is continuous in the domain, it is numerically computed with a characteristic method. The development of overturning is regulated with singular viscosity regularization (SVR), whose effect is to relocate the zeros of the level-set function close to the exact positions of the shock fronts in dam-break problems. The method is firstly verified with the explicitly known exact solutions of primitive dam-break problems, optimizing a parameter of SVR. Then, abrupt water release from Chan Thnal Reservoir, Kampong Speu Province, Cambodia into an initially dry bed of its irrigation canal system is simulated as a practical demonstrative example. In contrast to most of the available software tools using either the shallow water equations with some artificial viscosity or the diffusion wave approximation, the proposed method turns out to be free from spurious diffusive deformation of water surfaces even if relatively coarse computational mesh is used.

Capturing transformation of flood hazard over a large River Basin under changing climate using a top-down approach

Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026-2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.